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The insider threat problem is a significant and ever present issue faced by any organisation. While security mechanisms can be put in place to reduce the chances of external agents gaining access to a system, either to steal assets or alter records, the issue is more complex in tackling insider threat. If an employee already has legitimate access rights to a system, it is much more difficult to prevent them from carrying out inappropriate acts, as it is hard to determine whether the acts are part of their official work or indeed malicious. We present in this paper the concept of “Ben-ware”: a beneficial software system that uses low-level data collection from employees’ computers, along with Artifi- cial Intelligence, to identify anomalous behaviour of an employee. By comparing each employee’s activities against their own ‘normal’ profile, as well as against the organisational’s norm, we can detect those that are significantly divergent, which might indicate malicious activities. Dealing with false positives is one of the main challenges here. Anomalous behaviour could indicate malicious activities (such as an employee trying to steal confidential information), but they could also be be- nign (for example, an employee is carrying out a workaround or taking a shortcut to complete their job). Therefore it is important to minimise the risk of false positives, and we do this by combining techniques from human factors, artificial intelligence, and risk analysis in our approach. Developed as a distributed system, Ben-ware has a three-tier architecture composed of (i) probes for data col- lection, (ii) intermediate nodes for data routing, and (iii) high level nodes for data analysis …